Large data sets may exist in various sizes and with various levels of organization. With large data comprising data sets as large as ever, the volume of data collected incident to the increased popularity of online and electronic transactions continues to grow. Billions of rows and hundreds of thousands of columns worth of data may populate a single table, for example. An example of the use of large data is in assembling test data sets to perform analysis of transaction data, which is frequently a key priority for transaction account issuers. In that regard, transactions processed by the transaction account issuer are massive in volume and comprise tremendously large data sets.
Large data sets may have challenges. For example, a user may desire to retrieve a test data set for analysis of transaction data. The user may want to limit a test data set to a subset of fields otherwise available in the large data set. The process of determining the desired limitations is frequently time consuming. Moreover, the process of sorting and filtering the large data set to conform to the desired limitations, then providing it to the proper user is also time consuming and uses a large amount of computing resources, particularly if the data is desired to be updated at some interval. These limitations often hamper the availability of test data sets, result in use of stale test data, and frustrate and confuse the analysis of the transaction data, which obscures identification of real-world entities and individuals behind transactions.